scholarly journals A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4733
Author(s):  
Cuiping Shao ◽  
Huiyun Li ◽  
Zheng Wang ◽  
Jiayan Fang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Error Resilience ◽  
Component Analysis ◽  
Cmos Technology ◽  
Error Correcting Codes ◽  
Memory Storage ◽  
Protection Scheme ◽  
Bit Flip

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

Detection and monitoring of hydrothermal alteration by Principal Component Analysis applied on UAS derived optical data, Vulcano Island - Italy

2021 ◽  
Author(s):  
Daniel Müller ◽  
Stefan Bredemeyer ◽  
Edgar Zorn ◽  
Erica De Paolo ◽  
Thomas Walter
Keyword(s):  
Principal Component Analysis ◽  
High Resolution ◽  
Hydrothermal Alteration ◽  
High Efficiency ◽  
Principal Component ◽  
Component Analysis ◽  
Hydrothermal Systems ◽  
Unmanned Aircraft ◽  
Optical Data ◽  
Vulcano Island

<p>Modern UAS (unmanned aircraft system), light weight sensor systems and new processing routines allow us to gather optical data of volcanoes at a high resolution. However, due to the typically poor colorization, our ability to investigate and interpret such data is limited. Further, the information stored in the red, green and blue channel (RGB) is correlated. This makes any analysis a 3 dimensional task. Principal Component Analysis (PCA) helps us to overcome these problems by decorrelating the original band information and generating a variance representation of the original data. Therefore PCA is a suitable tool to detect optical anomalies, as might be caused by volcanic degassing and associated processes.</p><p>Applied in a case study at La Fossa Cone (Vulcano Island - Italy), the PCA showed a high efficiency for the detection and pixel based extraction of areas subject to hydrothermal alteration and sulfur deposition. We observed a broad alteration zone surrounding the active fumarole field, but also heterogeneities within, indicating a segmentation. Systematic variations in color and density distribution of sulfur deposits have implications for structural controls on the degassing system.</p><p>Combining the efficiency of PCA with the high resolution of UAS derived data, this methodology has a high potential to be employed in the spatio-temporal monitoring of volcanic hydrothermal systems and processes at surface.</p><p> </p>

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